Research projects in Information Technology

There is an opportunity to prototype digital water play systems and examine users’ aquatic body-environment interactions to derive an understanding of digital technology’s opportunities to facilitate novel bodily water play interactions in-water, on-water and underwater. 

An interest and experience with water-based activities, interactive technology, hardware prototyping (including actuators), human movement/performance and aquatic culture (including diving) is desirable. This work is in collaboration with Dr. Sarah Jane Pell (sarahjanepell.com).

With drones getting smaller and smaller, we regard them as physical pixels that can be placed anywhere in space, allowing us to experience digital content in the physical world in novel playful ways.

These projects will utilize the lab’s Qualisys motion capture system and Crazyflie mini quadcopters. A passion for robotics including hard- and software design for quadcopters and motion capture is desirable.

The result will be a thesis in the field of interaction design, contributing to our understanding of experiencing the human body as play. 

There is an opportunity to enrich indoor rock-climbing or bouldering through interactive technology. This project builds on prior work and combines bouldering with Hololens and motion capture.

The candidate will engage with a dedicated bouldering wall in our lab, together with AR, biosensors, etc. and study the associated “humility” experiences. 

The result will be a thesis in the field of interaction design, contributing to our understanding of experiencing the human body as play. 

More information at http://exertiongameslab.org

This practice-based research involves further development of the AirSticks, a hardware/software package which allows the triggering and manipulation of sound and visuals in a 3D playing space, as a gestural instrument for live electronic music performance, music education and general health and wellbeing in collaboration with our interdisciplinary team at SensiLab. This can be done through new performances, new software or new hardware. How can we reinvent the connect between our bodies, our ears and our creativity, and what new applications for the AirSticks can be discovered?

This project will seek to further the research into and development of machine learning techniques that may be used to triage, classify, and otherwise process material of a distressing nature (such as child exploitation material). It will involve the use of deep neural networks for image, video, audio, social network, and/or text classification.

This project aims to identify novel methods for inferring where and when photographs and videos were recorded from features of the material itself. A key requirement of image processing in a Law Enforcement (LE) context is to augment classification of material by identifying its spatio-temporal context.

We live and work in a world of complex relationships between data, systems, knowledge, people, documents, biology, software, society, politics, commerce and so on.  We can model these relationships as networks or graphs in the hope of reasoning about them - but the tools that we have for understanding such network structured data (whether algorithmic analytics or visualisation tools) remain crude.  Emerging display and interaction devices such as augmented and virtual reality headsets offer new ways to visualise and interact with data in the world around us rather than on screens.  This…

Testing self-driving cars is extremely difficult, as one has to account for a very large space of possible scenarios. In this project, we will explore the application of automated testing techniques, mainly in the area of search-based software testing to verify that the AI components of self-driving cars work as they should. This project is in collaboration with Professor Hai Vu and the Monash Connected Autonomous Vehicle (MCAV) team.

Adversarial Machine Learning (AML) is a technique to fool a machine learning model through malicious input. Due to its significance in many scenarios, including security, privacy, and health application, AML has attracted a large amount of attention in recent years. However, the underlying theoretical foundation for AML still remains unclear and how to design effective and efficient attack and defence algorithms are remain a challenge in the research community. Furthermore, most existing  AML algorithms can only apply to Euclidean space.

The brain is a complex system and monitoring and imaging methods to observe critical neurophysiological variables underlying brain function are limited. This project works at the intersection of statistical signal processing, inference, machine learning and dynamical systems theory to develop new semi-analtyical filtering approaches for state and parameter estimation to infer neurophysiological variables such as network connection strengths between neural population networks underlying brain activity.